1 Overview

1.1 Data inspection

1.2 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 790) ranged in age 18 to 79 (Mdnage = 35.00, Mage = 37.35, SD = 13.54). The participants were predominantly female (56.84%). The participants received £0.35 ($0.45) for successfully completing the task.

1.3 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check
Item Dynamic Static No norm Significance test
Age (years) 37.65 \(\pm\) 14.19 38.16 \(\pm\) 13.00 36.24 \(\pm\) 13.41 \(F(2, 787) = 1.44\), \(\mathit{MSE} = 183.14\), \(p = .238\)
Gender (%) Male (40.23%) Female (59.38%) Other (0.39%) Male (42.16%) Female (57.84%) Male (46.62%) Female (53.38%) \(\chi^2(4, n = 790) = 4.29\), \(p = .368\)
Political position 3.50 \(\pm\) 1.22 3.54 \(\pm\) 1.26 3.45 \(\pm\) 1.28 \(F(2, 787) = 0.34\), \(\mathit{MSE} = 1.57\), \(p = .709\)
Nationality (%) 1 (85.16%) 2 (3.12%) 3 (9.77%) 4 (0.78%) NA (1.17%) 1 (80.6%) 2 (5.6%) 3 (8.58%) 4 (1.49%) NA (3.73%) 1 (80.08%) 2 (5.64%) 3 (9.4%) 4 (1.88%) NA (3.01%) \(\chi^2(6, n = 790) = 3.98\), \(p = .680\)

1.4 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures
Alpha M SD 1 2 3 4 5 6
Interest
3.54 1.80
Attitude 0.91 4.64 1.26 .82**
Intention 0.98 4.22 1.77 .84** .84**
Expectation 0.98 3.92 1.72 .81** .82** .92**
Intent/expectation composite
4.07 1.71 .84** .85** .98** .98**
Perception of change
5.18 0.78 .36** .35** .35** .34** .35**
Preconformity
4.14 1.15 .43** .40** .39** .36** .39** .46**

2 Confirmatory analyses

2.1 Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing meat consumption (compared to static norm salience)?

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from Mdiff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

World cloud of participants response to text

Figure 2.1: World cloud of participants response to text

Table 2.1:
Most frequent words in text
word freq
health 210.00
environment 181.00
animals 136.00
climate 61.00
awareness 60.00
concerns 60.00
impact 50.00
media 50.00
vegan 38.00
better 31.00

The mean interest for participants in the dynamic norm condition was M = 3.60 (SD = 1.83), and the mean interest in the static norm condition was M = 3.63 (SD = 1.83). The mean interest in the no norm condition was M = 3.39 (SD = 1.73).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.60, SD = 1.83) and static norm (M = 3.63, SD = 1.83) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.28]\), \(t(787) = -0.18\), \(p = .854\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.19, RR[0.69, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.39, SD = 1.73) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.21\), 95% CI \([-0.10\), \(0.52]\), \(t(787) = 1.31\), \(p = .190\), d = 0.12, or the static-norm condition \(\Delta M = 0.24\), 95% CI \([-0.07\), \(0.54]\), \(t(787) = 1.51\), \(p = .130\), d = 0.13. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.09\), 95% CI \([-0.18\), \(0.36]\), \(t(787) = 0.65\), \(p = .516\).

Table 2.2:
Meat consumption by condition contrasts
contrast \(\Delta M\) 95% CI \(t(787)\) \(p\)
DYST Dynamic, static -0.03 \([-0.34\), \(0.28]\) -0.18 .854
DYNO Dynamic, control 0.21 \([-0.10\), \(0.52]\) 1.31 .190
STNO Static, control 0.24 \([-0.07\), \(0.54]\) 1.51 .130
DYCONT Dynamic, both 0.09 \([-0.18\), \(0.36]\) 0.65 .516
EXPNO Norms, control -0.22 \([-0.49\), \(0.04]\) -1.63 .103

2.2 Will participants in the dynamic norm condition be more likely (than those in the static norm control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of Mdiff = 0.40. The plausible maximum effect was set at twice the predicted effect of Mdiff = 0.80. A Bayes factor was calculated for each test.

Table 2.3: Expectations of future meat consumption
Future Norm
Preconformity
Combined
Condition \(n\) \(M\) \(SD\) \(M\) \(SD\) \(M\) \(SD\)
Dynamic 256 5.32 0.79 4.30 1.14 4.81 0.85
Static 268 5.20 0.76 4.19 1.16 4.70 0.83
No norm 266 5.02 0.77 3.92 1.13 4.47 0.79

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.12\), 95% CI \([-0.01\), \(0.25]\), \(t(787) = 1.76\), \(p = .079\), d = 0.15, \(B_{\text{HN}(0, 0.40)}\) = 1.43, RR[0.05, 2]

Table 2.4: Perception change contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.12 \([-0.01\), \(0.25]\) 1.76 .079
DYNO Dynamic, control 0.29 \([0.16\), \(0.43]\) 4.34 < .001
STNO Static, control 0.18 \([0.04\), \(0.31]\) 2.62 .009
DYCONT Dynamic, both 0.21 \([0.09\), \(0.32]\) 3.51 < .001
EXPNO Norms, control -0.23 \([-0.35\), \(-0.12]\) -4.03 < .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.30, SD = 1.14) and static norm (M = 4.19, SD = 1.16) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(787) = 1.11\), \(p = .269\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.74, RR[0.05, 1.55].

Table 2.5: Preconformity contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.11 \([-0.09\), \(0.31]\) 1.11 .269
DYNO Dynamic, control 0.38 \([0.19\), \(0.58]\) 3.83 < .001
STNO Static, control 0.27 \([0.08\), \(0.47]\) 2.75 .006
DYCONT Dynamic, both 0.25 \([0.08\), \(0.42]\) 2.84 .005
EXPNO Norms, control -0.33 \([-0.50\), \(-0.16]\) -3.81 < .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.85) and static norm (M = 4.70, SD = 0.83) conditions, \(\Delta M = 0.11\), 95% CI \([-0.03\), \(0.26]\), \(t(787) = 1.59\), \(p = .111\), d = 0.14, \(B_{\text{HN}(0, 0.40)}\) = 1.14, RR[0.05, 2].

Table 2.6: Combined contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.11 \([-0.03\), \(0.26]\) 1.59 .111
DYNO Dynamic, control 0.34 \([0.20\), \(0.48]\) 4.70 < .001
STNO Static, control 0.22 \([0.08\), \(0.36]\) 3.15 .002
DYCONT Dynamic, both 0.23 \([0.10\), \(0.35]\) 3.63 < .001
EXPNO Norms, control -0.28 \([-0.40\), \(-0.16]\) -4.54 < .001

3 Secondary analyses

3.1 Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 130.379 p-value lower bound: 0e+00 t-value upper bound: \(-33.392\) p-value upper bound: \(6.46\times10^{-132}\) degrees of freedom : 521.6

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 2.86 upper bound 90% CI: 3.062

NHST confidence interval: lower bound 95% CI: 2.841 upper bound 95% CI: 3.081

Equivalence Test Result: The equivalence test was significant, t(521.6) = \(-33.392\), p = \(6.46\times10^{-132}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(521.6) = 48.493, p = \(2.11\times10^{-195}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.2 Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 14.613 p-value lower bound: \(4.35\times10^{-41}\) t-value upper bound: \(-13.059\) p-value upper bound: \(3.16\times10^{-34}\) degrees of freedom : 521.78

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.315\) upper bound 90% CI: 0.876

NHST confidence interval: lower bound 95% CI: \(-0.429\) upper bound 95% CI: 0.991

Equivalence Test Result: The equivalence test was significant, t(521.78) = \(-13.059\), p = \(3.16\times10^{-34}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(521.78) = 0.777, p = .438, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory analyses

4.1 Does dynamic norm (versus static norm) information lead to more positive attitudes, intentions, and expectations to reduce meat consumption?

4.1.1 Trace plots

Traceplots of regression parameters

Figure 4.1: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Posterior uncertainty intervals

Figure 4.4: Posterior uncertainty intervals

Posterior density plot

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)
Model 1: Uninformative priors\(^a\)
Model 2: Weakly informative priors\(^b\)
Model 3: Moderately informative priors\(^c\)
Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest 0.035 (0.154) -0.268, 0.335 738.292 1 normal(0,10) 0.088 (0.163) -0.237, 0.405 708.044 1 151.43 normal(0.5,0.75) 0.204 (0.135) -0.059, 0.479 757.104 1.003 482.86 normal(0.5, 0.35)
Attitude -0.036 (0.108) -0.249, 0.174 750.076 1.001 normal(0,10) -0.000 (0.115) -0.226, 0.223 728.624 1 -100 normal(0.5,0.75) 0.082 (0.098) -0.11, 0.273 727.912 1.005 -327.78 normal(0.5, 0.35)
Intention/Expectation -0.024 (0.143) -0.302, 0.257 721.838 1.001 normal(0,10) 0.025 (0.154) -0.273, 0.339 705.427 1 -204.17 normal(0.5,0.75) 0.136 (0.127) -0.118, 0.389 716.604 1.003 -666.67 normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .504 b ppp = .493 c ppp = .451

4.2 Does age interact with norm condition to influence dependent variables?

4.2.1 Trace plots

Traceplots of regression parameters

Figure 4.6: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.7: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.8: Traceplots of regression parameters

4.2.2 Posterior plots

Posterior uncertainty intervals

Figure 4.9: Posterior uncertainty intervals

4.2.3 Summary table

Table 4.2: Posterior results for multi-sample analysis by age (H4)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Young adults
Interest 0.223 (0.332) -0.411, 0.864 1468.645 1.004 normal(0,10) 0.349 (0.285) -0.208, 0.904 1080.827 1 56.5 normal(0.5,0.75) 0.484 (0.213) 0.066, 0.9 1543.916 1.001 117.04 normal(0.5,0.35)
Attitude 0.147 (0.236) -0.322, 0.611 1463.529 1.002 normal(0,10) 0.238 (0.202) -0.151, 0.647 1175.109 1.001 61.9 normal(0.5,0.75) 0.342 (0.165) 0.013, 0.667 1247.571 1.004 132.65 normal(0.5,0.35)
Intention/Expectation 0.046 (0.308) -0.544, 0.655 1394.423 1.005 normal(0,10) 0.168 (0.258) -0.329, 0.671 1045.390 1.001 265.22 normal(0.5,0.75) 0.307 (0.197) -0.078, 0.683 979.732 1.005 567.39 normal(0.5,0.35)
Middle-aged adults
Interest 0.027 (0.233) -0.439, 0.488 1280.253 1.002 normal(0,10) 0.139 (0.216) -0.27, 0.572 1009.412 1.006 414.81 normal(0.5,0.75) 0.314 (0.171) -0.023, 0.661 1188.646 1.003 1062.96 normal(0.5,0.35)
Attitude -0.106 (0.168) -0.438, 0.229 1210.094 1.002 normal(0,10) -0.024 (0.159) -0.334, 0.297 1033.752 1.008 -77.36 normal(0.5,0.75) 0.109 (0.128) -0.131, 0.365 1133.668 1.004 -202.83 normal(0.5,0.35)
Intention/Expectation -0.042 (0.223) -0.495, 0.393 1344.973 1.002 normal(0,10) 0.069 (0.209) -0.347, 0.484 962.467 1.007 -264.29 normal(0.5,0.75) 0.243 (0.166) -0.078, 0.574 1173.682 1.004 -678.57 normal(0.5,0.35)
Old adults
Interest -0.107 (0.283) -0.664, 0.444 1024.560 1.002 normal(0,10) 0.042 (0.254) -0.452, 0.529 872.720 1.007 -139.25 normal(0.5,0.75) 0.265 (0.185) -0.096, 0.628 979.422 1.002 -347.66 normal(0.5,0.35)
Attitude -0.073 (0.190) -0.445, 0.306 1036.491 1.002 normal(0,10) 0.024 (0.175) -0.321, 0.37 919.469 1.006 -132.88 normal(0.5,0.75) 0.175 (0.136) -0.093, 0.446 930.504 1.001 -339.73 normal(0.5,0.35)
Intention/Expectation -0.063 (0.260) -0.581, 0.448 1122.595 1.002 normal(0,10) 0.075 (0.233) -0.383, 0.537 897.787 1.006 -219.05 normal(0.5,0.75) 0.275 (0.178) -0.063, 0.631 939.502 1.002 -536.51 normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .488 b ppp = .485 c ppp = .429
Table 4.3: Posterior results for moderation model using age as continuous variable (H4)
Estimate Post.SD pi.lower pi.upper Rhat neff Prior
Interest
conditionbi 0.049 0.160 -0.265 0.354 1.005 720.555 normal(0,10)
age_cent -0.018 0.009 -0.036 -0.001 1.002 1194.944 normal(0,10)
condition_age 0.010 0.012 -0.014 0.032 1.002 1105.091 normal(0,10)
Attitudes
conditionbi -0.031 0.114 -0.255 0.196 1.006 732.785 normal(0,10)
age_cent -0.007 0.006 -0.02 0.005 1.001 1218.820 normal(0,10)
condition_age 0.005 0.008 -0.012 0.021 1.001 1111.234 normal(0,10)
Intentions/Expectations
conditionbi -0.026 0.148 -0.315 0.26 1.007 719.482 normal(0,10)
age_cent -0.001 0.008 -0.017 0.014 1.002 1111.115 normal(0,10)
condition_age 0.000 0.011 -0.022 0.021 1.002 1070.955 normal(0,10)

4.3 Do demographic factors such as age, gender, and political position predict the primary dependent variables?

4.3.1 Trace plots

Traceplots for estimated regression parameters

Figure 4.10: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.11: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.12: Traceplots for estimated regression parameters

4.3.2 Posterior plots

Posterior uncertainty intervals

Figure 4.13: Posterior uncertainty intervals

Posterior density plot

Figure 4.14: Posterior density plot

4.3.3 Summary table

Table 4.4: Posterior results for full model (H5)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest
Condition 0.042 (0.153) -0.261, 0.338 1326.839 1.002 normal(0,10) 0.088 (0.148) -0.198, 0.38 1482.906 1.001 109.52 normal(0.5,0.75) 0.207 (0.134) -0.056, 0.478 1444.273 1.006 392.86 normal(0.5,0.35)
Age -0.006 (0.006) -0.017, 0.006 2207.291 1 normal(0,10) -0.006 (0.006) -0.018, 0.005 2397.144 1 0 normal(0,10) -0.006 (0.006) -0.016, 0.006 2415.903 0.999 0 normal(0,10)
Gender 0.491 (0.158) 0.187, 0.787 1391.653 1.002 normal(0,10) 0.499 (0.154) 0.201, 0.796 1074.244 1.001 1.63 normal(0,10) 0.493 (0.162) 0.184, 0.813 1179.136 1.001 0.41 normal(0,10)
Politics -0.286 (0.065) -0.416, -0.156 1437.685 1.001 normal(0,10) -0.289 (0.063) -0.413, -0.164 1414.559 1 1.05 normal(0,10) -0.289 (0.063) -0.418, -0.162 1445.607 1 1.05 normal(0,10)
Attitudes
Condition -0.034 (0.106) -0.237, 0.167 1406.600 1.002 normal(0,10) -0.002 (0.104) -0.207, 0.199 1354.936 1.002 -94.12 normal(0.5,0.75) 0.082 (0.097) -0.113, 0.273 1493.845 1.006 -341.18 normal(0.5,0.35)
Age 0.001 (0.004) -0.008, 0.009 2317.231 1 normal(0,10) 0.001 (0.004) -0.008, 0.008 2343.693 1 0 normal(0,10) 0.001 (0.004) -0.007, 0.009 2431.615 0.999 0 normal(0,10)
Gender 0.308 (0.113) 0.09, 0.522 1391.416 1.002 normal(0,10) 0.313 (0.109) 0.102, 0.527 959.334 1.001 1.62 normal(0,10) 0.308 (0.115) 0.087, 0.527 1061.106 1 0 normal(0,10)
Politics -0.238 (0.046) -0.327, -0.149 1454.913 1 normal(0,10) -0.239 (0.045) -0.328, -0.153 1352.135 1.001 0.42 normal(0,10) -0.239 (0.045) -0.324, -0.149 1431.927 1.001 0.42 normal(0,10)
Intention/Expectations
Condition -0.021 (0.143) -0.309, 0.252 1404.500 1.004 normal(0,10) 0.020 (0.139) -0.248, 0.292 1363.134 1.002 -195.24 normal(0.5,0.75) 0.134 (0.128) -0.113, 0.384 1458.703 1.005 -738.1 normal(0.5,0.35)
Age 0.006 (0.006) -0.005, 0.016 2133.728 1 normal(0,10) 0.005 (0.006) -0.006, 0.016 2250.153 1 -16.67 normal(0,10) 0.006 (0.005) -0.005, 0.016 2527.464 1 0 normal(0,10)
Gender 0.603 (0.151) 0.307, 0.898 1422.070 1.003 normal(0,10) 0.610 (0.145) 0.326, 0.895 1058.218 1.001 1.16 normal(0,10) 0.602 (0.154) 0.305, 0.899 1118.308 1.001 -0.17 normal(0,10)
Politics -0.258 (0.061) -0.379, -0.143 1346.709 1 normal(0,10) -0.259 (0.060) -0.38, -0.14 1328.150 1 0.39 normal(0,10) -0.260 (0.058) -0.376, -0.141 1378.480 1 0.78 normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .501 b ppp = .497 c ppp = .450

5 Unregistered analyses

5.1 Power test

Sparkman and Walton (2017) found standardized effects of dynamic norms on interest in reducing meat consumption ranging from \(d\) = 0.31 – 0.41. To detect the average effect of \(d\) = 0.36, we would need 122 participants in each condition.

 Two-sample t test power calculation 

          n = 122.0922
          d = 0.36
  sig.level = 0.05
      power = 0.8
alternative = two.sided

NOTE: n is number in each group

5.2 Moderation of demographic variables

Table 5.1:
Exploring the effect moderation of demographic variables on attitudes
Predictor \(b\) 95% CI \(t(517)\) \(p\)
Intercept 5.40 \([4.88\), \(5.92]\) 20.31 < .001
ConditionbiStatic -0.16 \([-0.88\), \(0.56]\) -0.44 .658
GenderbiFemale 0.39 \([0.08\), \(0.71]\) 2.44 .015
POLITICS -0.27 \([-0.40\), \(-0.15]\) -4.21 < .001
ConditionbiStatic \(\times\) GenderbiFemale -0.17 \([-0.61\), \(0.27]\) -0.78 .438
ConditionbiStatic \(\times\) POLITICS 0.07 \([-0.11\), \(0.24]\) 0.74 .462
Table 5.2:
Exploring the effect moderation of demographic variables on intention
Predictor \(b\) 95% CI \(t(517)\) \(p\)
Intercept 4.73 \([4.04\), \(5.42]\) 13.41 < .001
ConditionbiStatic -0.23 \([-1.19\), \(0.72]\) -0.48 .632
GenderbiFemale 0.80 \([0.38\), \(1.22]\) 3.76 < .001
POLITICS -0.32 \([-0.49\), \(-0.15]\) -3.70 < .001
ConditionbiStatic \(\times\) GenderbiFemale -0.43 \([-1.01\), \(0.16]\) -1.43 .152
ConditionbiStatic \(\times\) POLITICS 0.13 \([-0.10\), \(0.36]\) 1.11 .266
Table 5.3:
Exploring the effect moderation of demographic variables on interest
predictor \(b\) 95% CI \(t(517)\) \(p\)
Intercept 4.53 \([3.79\), \(5.27]\) 12.05 < .001
Condition -0.28 \([-1.30\), \(0.74]\) -0.54 .589
Gender 0.54 \([0.09\), \(0.98]\) 2.36 .019
Political position -0.35 \([-0.53\), \(-0.17]\) -3.87 < .001
Condition \(\times\) Gender -0.08 \([-0.70\), \(0.54]\) -0.25 .803
Condition \(\times\) Political position 0.10 \([-0.14\), \(0.35]\) 0.83 .408

5.3 Mediation

Table 5.4: Exploring mediating effect of attitude on intentions/expectations
Parameter \(M\) \(SD\) Lower PPI Upper PPI Rhat Prior
Direct effects
Interest ~ Condition 0.028 0.160 -0.294 0.335 1.001 normal(0,10)
Attitude ~ Condition -0.041 0.109 -0.255 0.175 1.002 normal(0,10)
Intent/Expectation ~ Condition -0.005 0.097 -0.198 0.181 1.001 normal(0,10)
Intent/Expectation ~ Attitude 0.630 0.047 0.533 0.719 1.002 normal(0,10)
Mediation
Indirect effects/nCondition > Attitude > Intention/Expectation -0.026 0.069 -0.161 0.11 NA
Total effect -0.030 0.119 -0.264 0.203 NA

6 Environment and data

6.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-07-19                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                               
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.1.0)                       
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.1.0)                       
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.1.0)                       
##  bayesplot      1.8.1      2021-06-14 [1] CRAN (R 4.1.0)                       
##  bfrr         * 0.0.0.9000 2021-06-29 [1] Github (debruine/bfrr@9b80a99)       
##  bitops         1.0-7      2021-04-24 [1] CRAN (R 4.1.0)                       
##  bookdown       0.22       2021-04-22 [1] CRAN (R 4.1.0)                       
##  boot           1.3-28     2021-05-03 [1] CRAN (R 4.1.0)                       
##  broom          0.7.8      2021-06-24 [1] CRAN (R 4.1.0)                       
##  bslib          0.2.5.1    2021-05-18 [1] CRAN (R 4.1.0)                       
##  cachem         1.0.5      2021-05-15 [1] CRAN (R 4.1.0)                       
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.1.0)                       
##  car            3.0-11     2021-06-27 [1] CRAN (R 4.1.0)                       
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.1.0)                       
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.1.0)                       
##  cli            3.0.1      2021-07-17 [1] CRAN (R 4.1.0)                       
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.1.0)                       
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.1.0)                       
##  colorspace     2.0-2      2021-06-24 [1] CRAN (R 4.1.0)                       
##  crayon         1.4.1      2021-02-08 [1] CRAN (R 4.1.0)                       
##  curl           4.3.2      2021-06-23 [1] CRAN (R 4.1.0)                       
##  data.table     1.14.0     2021-02-21 [1] CRAN (R 4.1.0)                       
##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.1.0)                       
##  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.1.0)                       
##  desc           1.3.0      2021-03-05 [1] CRAN (R 4.1.0)                       
##  devtools       2.4.2      2021-06-07 [1] CRAN (R 4.1.0)                       
##  digest         0.6.27     2020-10-24 [1] CRAN (R 4.1.0)                       
##  dplyr        * 1.0.7      2021-06-18 [1] CRAN (R 4.1.0)                       
##  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.1.0)                       
##  emmeans      * 1.6.2-1    2021-07-08 [1] CRAN (R 4.1.0)                       
##  estimability   1.3        2018-02-11 [1] CRAN (R 4.1.0)                       
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.1.0)                       
##  ez             4.4-0      2016-11-02 [1] CRAN (R 4.1.0)                       
##  fansi          0.5.0      2021-05-25 [1] CRAN (R 4.1.0)                       
##  farver         2.1.0      2021-02-28 [1] CRAN (R 4.1.0)                       
##  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.1.0)                       
##  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.1.0)                       
##  foreign        0.8-81     2020-12-22 [1] CRAN (R 4.1.0)                       
##  fs             1.5.0      2020-07-31 [1] CRAN (R 4.1.0)                       
##  generics       0.1.0      2020-10-31 [1] CRAN (R 4.1.0)                       
##  ggplot2      * 3.3.5      2021-06-25 [1] CRAN (R 4.1.0)                       
##  ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.1.0)                       
##  glue           1.4.2      2020-08-27 [1] CRAN (R 4.1.0)                       
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.1.0)                       
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.1.0)                       
##  haven          2.4.1      2021-04-23 [1] CRAN (R 4.1.0)                       
##  here         * 1.0.1      2020-12-13 [1] CRAN (R 4.1.0)                       
##  highr          0.9        2021-04-16 [1] CRAN (R 4.1.0)                       
##  hms            1.1.0      2021-05-17 [1] CRAN (R 4.1.0)                       
##  htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.1.0)                       
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.1.0)                       
##  insight        0.14.2     2021-06-22 [1] CRAN (R 4.1.0)                       
##  jquerylib      0.1.4      2021-04-26 [1] CRAN (R 4.1.0)                       
##  jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.1.0)                       
##  kableExtra   * 1.3.4.9000 2021-07-03 [1] Github (haozhu233/kableExtra@4c93f1a)
##  knitr        * 1.33       2021-04-24 [1] CRAN (R 4.1.0)                       
##  labeling       0.4.2      2020-10-20 [1] CRAN (R 4.1.0)                       
##  labelled       2.8.0      2021-03-08 [1] CRAN (R 4.1.0)                       
##  lattice        0.20-44    2021-05-02 [1] CRAN (R 4.1.0)                       
##  lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.1.0)                       
##  lme4           1.1-27.1   2021-06-22 [1] CRAN (R 4.1.0)                       
##  lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.1.0)                       
##  magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.1.0)                       
##  MASS         * 7.3-54     2021-05-03 [1] CRAN (R 4.1.0)                       
##  Matrix         1.3-4      2021-06-01 [1] CRAN (R 4.1.0)                       
##  MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.1.0)                       
##  memoise        2.0.0      2021-01-26 [1] CRAN (R 4.1.0)                       
##  mgcv           1.8-36     2021-06-01 [1] CRAN (R 4.1.0)                       
##  minqa          1.2.4      2014-10-09 [1] CRAN (R 4.1.0)                       
##  mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.1.0)                       
##  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.1.0)                       
##  MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.1.0)                       
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.1.0)                       
##  mvtnorm        1.1-2      2021-06-07 [1] CRAN (R 4.1.0)                       
##  nlme           3.1-152    2021-02-04 [1] CRAN (R 4.1.0)                       
##  nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.1.0)                       
##  NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.1.0)                       
##  openxlsx       4.2.4      2021-06-16 [1] CRAN (R 4.1.0)                       
##  papaja       * 0.1.0.9997 2021-06-11 [1] Github (crsh/papaja@a231c36)         
##  pillar         1.6.1      2021-05-16 [1] CRAN (R 4.1.0)                       
##  pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.1.0)                       
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.1.0)                       
##  pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.1.0)                       
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.1.0)                       
##  prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.1.0)                       
##  processx       3.5.2      2021-04-30 [1] CRAN (R 4.1.0)                       
##  ps             1.6.0      2021-02-28 [1] CRAN (R 4.1.0)                       
##  psy          * 1.1        2012-06-21 [1] CRAN (R 4.1.0)                       
##  psych        * 2.1.6      2021-06-18 [1] CRAN (R 4.1.0)                       
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)                       
##  qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.1.0)                       
##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.1.0)                       
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.1.0)                       
##  Rcpp         * 1.0.7      2021-07-07 [1] CRAN (R 4.1.0)                       
##  RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.1.0)                       
##  readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.1.0)                       
##  readxl         1.3.1      2019-03-13 [1] CRAN (R 4.1.0)                       
##  remotes        2.4.0      2021-06-02 [1] CRAN (R 4.1.0)                       
##  reprex         2.0.0      2021-04-02 [1] CRAN (R 4.1.0)                       
##  reshape        0.8.8      2018-10-23 [1] CRAN (R 4.1.0)                       
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.1.0)                       
##  rio            0.5.27     2021-06-21 [1] CRAN (R 4.1.0)                       
##  rlang        * 0.4.11     2021-04-30 [1] CRAN (R 4.1.0)                       
##  rmarkdown      2.9        2021-06-15 [1] CRAN (R 4.1.0)                       
##  rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.1.0)                       
##  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.1.0)                       
##  rvest          1.0.0      2021-03-09 [1] CRAN (R 4.1.0)                       
##  sass           0.4.0      2021-05-12 [1] CRAN (R 4.1.0)                       
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.1.0)                       
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.1.0)                       
##  sjlabelled     1.1.8      2021-05-11 [1] CRAN (R 4.1.0)                       
##  slam           0.1-48     2020-12-03 [1] CRAN (R 4.1.0)                       
##  SnowballC    * 0.7.0      2020-04-01 [1] CRAN (R 4.1.0)                       
##  stringi        1.7.3      2021-07-16 [1] CRAN (R 4.1.0)                       
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.1.0)                       
##  svglite        2.0.0      2021-02-20 [1] CRAN (R 4.1.0)                       
##  systemfonts    1.0.2      2021-05-11 [1] CRAN (R 4.1.0)                       
##  testthat       3.0.4      2021-07-01 [1] CRAN (R 4.1.0)                       
##  tibble       * 3.1.2      2021-05-16 [1] CRAN (R 4.1.0)                       
##  tidyr        * 1.1.3      2021-03-03 [1] CRAN (R 4.1.0)                       
##  tidyselect     1.1.1      2021-04-30 [1] CRAN (R 4.1.0)                       
##  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.1.0)                       
##  tm           * 0.7-8      2020-11-18 [1] CRAN (R 4.1.0)                       
##  tmvnsim        1.0-2      2016-12-15 [1] CRAN (R 4.1.0)                       
##  TOSTER       * 0.3.4      2018-08-03 [1] CRAN (R 4.1.0)                       
##  usethis        2.0.1      2021-02-10 [1] CRAN (R 4.1.0)                       
##  utf8           1.2.1      2021-03-12 [1] CRAN (R 4.1.0)                       
##  vctrs          0.3.8      2021-04-29 [1] CRAN (R 4.1.0)                       
##  viridisLite    0.4.0      2021-04-13 [1] CRAN (R 4.1.0)                       
##  webshot        0.5.2      2019-11-22 [1] CRAN (R 4.1.0)                       
##  withr          2.4.2      2021-04-18 [1] CRAN (R 4.1.0)                       
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.1.0)                       
##  xfun           0.24       2021-06-15 [1] CRAN (R 4.1.0)                       
##  XML          * 3.99-0.6   2021-03-16 [1] CRAN (R 4.1.0)                       
##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.1.0)                       
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.1.0)                       
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.1.0)                       
##  zip            2.2.0      2021-05-31 [1] CRAN (R 4.1.0)                       
## 
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

6.2 Codebook

6.2.1 Metadata

6.2.1.1 Description

Dataset name: results

The dataset has N=790 rows and 34 columns. 0 rows have no missing values on any column.

Metadata for search engines
  • Date published: 2021-07-19
x
DYUK
STUK
UKNATION
RESIDENT
GENDER
condition
conditionbi
genderbi
age_cat
NATIONALITY
INTEREST
ATT1
ATT2
ATT3
INTENT1
INTENT2
INTENT3
EXPECT1
EXPECT2
EXPECT3
PERCEPTNUM
PERCEPTSCALE
CONSTRUAL_1
PERCEPTCHANGE
PRECONFORMITY
POLITICS
AGE
attitude_mean
intention_mean
expect_mean
expintent_avg
age_cent
PERCEPTCHANGE_r
comb_future

6.3 Codebook table

name data_type ordered value_labels n_missing complete_rate n_unique empty top_counts min median max mean sd whitespace hist DYUK STUK NATIONALITY label
<a href=“#DYUK”>DYUK</a> character NA NA 534 0.3240506 256 0 NA 7 NA 898 NA NA 0 NA Dynamic UK NA NA NA
<a href=“#STUK”>STUK</a> character NA NA 522 0.3392405 266 0 NA 5 NA 993 NA NA 0 NA NA Static UK NA NA
<a href=“#UKNATION”>UKNATION</a> factor FALSE
  1. 1,<br>2. 2,<br>3. 3,<br>4. 4
21 0.9734177 4 NA 1: 647, 3: 73, 2: 38, 4: 11 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#RESIDENT”>RESIDENT</a> factor FALSE
  1. 1,<br>2. 2
0 1.0000000 2 NA 1: 783, 2: 7 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#GENDER”>GENDER</a> factor FALSE
  1. Male,<br>2. Female,<br>3. Other
0 1.0000000 3 NA Fem: 449, Mal: 340, Oth: 1 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#condition”>condition</a> factor FALSE
  1. Dynamic,<br>2. Static,<br>3. No norm
0 1.0000000 3 NA Sta: 268, No : 266, Dyn: 256 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#conditionbi”>conditionbi</a> factor FALSE
  1. Dynamic,<br>2. Static
266 0.6632911 2 NA Sta: 268, Dyn: 256 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#genderbi”>genderbi</a> factor FALSE
  1. Male,<br>2. Female
1 0.9987342 2 NA Fem: 449, Mal: 340 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#age_cat”>age_cat</a> factor FALSE
  1. Young adults,<br>2. Middle-aged adults,<br>3. Old adults
0 1.0000000 3 NA Mid: 381, Old: 224, You: 185 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#NATIONALITY”>NATIONALITY</a> numeric NA NA 0 1.0000000 NA NA NA 3 27.0 183 28.0860759 11.9341320 NA ▇▁▁▁▁ NA NA Nationality NA
<a href=“#INTEREST”>INTEREST</a> numeric NA NA 0 1.0000000 NA NA NA 1 3.0 7 3.5417722 1.7999160 NA ▇▅▅▃▅ NA NA NA NA
<a href=“#ATT1”>ATT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.4607595 1.4303799 NA ▂▂▅▇▅ NA NA NA NA
<a href=“#ATT2”>ATT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.5772152 1.3560127 NA ▂▃▆▇▆ NA NA NA NA
<a href=“#ATT3”>ATT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.8734177 1.3212420 NA ▁▂▅▇▇ NA NA NA NA
<a href=“#INTENT1”>INTENT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1721519 1.7993758 NA ▆▃▅▇▇ NA NA NA NA
<a href=“#INTENT2”>INTENT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1392405 1.7788661 NA ▆▃▅▇▆ NA NA NA NA
<a href=“#INTENT3”>INTENT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.3367089 1.8168468 NA ▆▂▃▇▇ NA NA NA NA
<a href=“#EXPECT1”>EXPECT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8848101 1.7551365 NA ▇▃▅▇▅ NA NA NA NA
<a href=“#EXPECT2”>EXPECT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8493671 1.7502760 NA ▇▃▅▇▅ NA NA NA NA
<a href=“#EXPECT3”>EXPECT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.0227848 1.7212568 NA ▆▅▅▇▆ NA NA NA NA
<a href=“#PERCEPTNUM”>PERCEPTNUM</a> numeric NA NA 0 1.0000000 NA NA NA 3 30.0 60 27.9772152 9.4585742 NA ▁▃▇▂▁ NA NA NA NA
<a href=“#PERCEPTSCALE”>PERCEPTSCALE</a> numeric NA NA 0 1.0000000 NA NA NA 1 2.0 5 2.5151899 0.6897013 NA ▁▇▅▁▁ NA NA NA NA
<a href=“#CONSTRUAL_1”>CONSTRUAL_1</a> numeric NA NA 0 1.0000000 NA NA NA 1 10.5 21 11.1048101 4.1314985 NA ▂▆▇▇▂ NA NA NA NA
<a href=“#PERCEPTCHANGE”>PERCEPTCHANGE</a> numeric NA NA 0 1.0000000 NA NA NA 1 3.0 5 2.8227848 0.7809437 NA ▁▃▇▂▁ NA NA NA NA
<a href=“#PRECONFORMITY”>PRECONFORMITY</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.1379747 1.1546790 NA ▂▆▇▇▂ NA NA NA NA
<a href=“#POLITICS”>POLITICS</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.4987342 1.2533851 NA ▆▆▇▃▁ NA NA NA NA
<a href=“#AGE”>AGE</a> numeric NA NA 0 1.0000000 NA NA NA 18 35.0 79 37.3481013 13.5404980 NA ▇▆▅▂▁ NA NA NA NA
<a href=“#attitude_mean”>attitude_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.6371308 1.2576647 NA ▁▂▅▇▃ NA NA NA NA
<a href=“#intention_mean”>intention_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.7 7 4.2160338 1.7679082 NA ▅▃▅▇▆ NA NA NA NA
<a href=“#expect_mean”>expect_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.9189873 1.7166291 NA ▆▅▅▇▃ NA NA NA NA
<a href=“#expintent_avg”>expintent_avg</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.3 7 4.0675105 1.7084989 NA ▅▃▅▇▅ NA NA NA NA
<a href=“#age_cent”>age_cent</a> numeric NA NA 0 1.0000000 NA NA NA -19 -2.0 42 0.3356273 13.5404980 NA ▇▆▅▂▁ NA NA NA NA
<a href=“#PERCEPTCHANGE_r”>PERCEPTCHANGE_r</a> numeric NA NA 0 1.0000000 NA NA NA 3 5.0 7 5.1772152 0.7809437 NA ▁▂▇▃▁ NA NA NA NA
<a href=“#comb_future”>comb_future</a> numeric NA NA 0 1.0000000 NA NA NA 2 4.5 7 4.6575949 0.8334951 NA ▁▆▇▃▁ NA NA NA NA
JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "results",
  "datePublished": "2021-07-19",
  "description": "The dataset has N=790 rows and 34 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       534|\n|STUK            |NA    |       522|\n|UKNATION        |NA    |        21|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       266|\n|genderbi        |NA    |         1|\n|age_cat         |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "age_cat", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. Male,\n2. Female,\n3. Other",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. Male,\n2. Female",
      "@type": "propertyValue"
    },
    {
      "name": "age_cat",
      "value": "1. Young adults,\n2. Middle-aged adults,\n3. Old adults",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    }
  ]
}`